Installing Libraries¶

In [3]:
!pip install geopandas

Requirement already satisfied: geopandas in c:\users\silvi\anaconda3\lib\site-packages (0.14.1)
Requirement already satisfied: pyproj>=3.3.0 in c:\users\silvi\anaconda3\lib\site-packages (from geopandas) (3.6.1)
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Requirement already satisfied: fiona>=1.8.21 in c:\users\silvi\anaconda3\lib\site-packages (from geopandas) (1.9.5)
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Importing Libraries¶

In [5]:
import numpy as np
import pandas as pd
import geopandas as gpd
!pip install folium
import folium
from folium import plugins
import datetime

from IPython.display import HTML, display

from folium.plugins import TimestampedGeoJson

Requirement already satisfied: folium in c:\users\silvi\anaconda3\lib\site-packages (0.15.0)
Requirement already satisfied: jinja2>=2.9 in c:\users\silvi\anaconda3\lib\site-packages (from folium) (2.11.3)
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Requirement already satisfied: requests in c:\users\silvi\anaconda3\lib\site-packages (from folium) (2.23.0)
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Requirement already satisfied: certifi>=2017.4.17 in c:\users\silvi\anaconda3\lib\site-packages (from requests->folium) (2022.9.14)
Requirement already satisfied: chardet<4,>=3.0.2 in c:\users\silvi\anaconda3\lib\site-packages (from requests->folium) (3.0.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\users\silvi\anaconda3\lib\site-packages (from requests->folium) (1.25.11)

Importing the data as Pandas DataFrame¶

In [6]:
fp = r"C:\Users\silvi\Downloads\query.csv"
eartt=pd.read_csv(fp)
eartt.head()
Out[6]:
time latitude longitude depth mag magType nst gap dmin rms ... updated place type horizontalError depthError magError magNst status locationSource magSource
0 2021-01-23T23:36:50.808Z -61.8250 -55.4937 9.58 6.9 mww NaN 19.0 1.550 0.61 ... 2021-01-25T08:55:31.875Z South Shetland Islands earthquake 7.4 2.7 0.043 52.0 reviewed us us
1 2021-01-21T12:23:05.774Z 5.0074 127.5174 95.81 7.0 mww NaN 28.0 2.813 0.82 ... 2021-01-24T22:57:52.349Z 210 km SE of Pondaguitan, Philippines earthquake 7.0 5.0 0.069 20.0 reviewed us us
2 2021-01-19T02:46:22.380Z -31.8232 -68.8182 20.00 6.4 mww NaN 21.0 0.299 0.99 ... 2021-01-26T04:16:44.654Z 27 km SW of Pocito, Argentina earthquake 2.8 1.7 0.055 32.0 reviewed us us
3 2021-01-14T18:28:18.081Z -2.9711 118.8893 18.00 6.2 mww NaN 28.0 2.204 1.12 ... 2021-01-26T21:18:07.234Z 32 km S of Mamuju, Indonesia earthquake 5.5 1.0 0.066 22.0 reviewed us us
4 2021-01-11T21:32:59.020Z 51.2774 100.4370 10.00 6.7 mww NaN 22.0 2.045 0.75 ... 2021-01-24T04:35:19.831Z 30 km SSW of Turt, Mongolia earthquake 7.0 1.7 0.039 62.0 reviewed us us

5 rows × 22 columns

Rename the name of the column¶

In [7]:
eartt1=eartt.rename({'time':'time_hour'}, axis=1)
eartt1.head()
Out[7]:
time_hour latitude longitude depth mag magType nst gap dmin rms ... updated place type horizontalError depthError magError magNst status locationSource magSource
0 2021-01-23T23:36:50.808Z -61.8250 -55.4937 9.58 6.9 mww NaN 19.0 1.550 0.61 ... 2021-01-25T08:55:31.875Z South Shetland Islands earthquake 7.4 2.7 0.043 52.0 reviewed us us
1 2021-01-21T12:23:05.774Z 5.0074 127.5174 95.81 7.0 mww NaN 28.0 2.813 0.82 ... 2021-01-24T22:57:52.349Z 210 km SE of Pondaguitan, Philippines earthquake 7.0 5.0 0.069 20.0 reviewed us us
2 2021-01-19T02:46:22.380Z -31.8232 -68.8182 20.00 6.4 mww NaN 21.0 0.299 0.99 ... 2021-01-26T04:16:44.654Z 27 km SW of Pocito, Argentina earthquake 2.8 1.7 0.055 32.0 reviewed us us
3 2021-01-14T18:28:18.081Z -2.9711 118.8893 18.00 6.2 mww NaN 28.0 2.204 1.12 ... 2021-01-26T21:18:07.234Z 32 km S of Mamuju, Indonesia earthquake 5.5 1.0 0.066 22.0 reviewed us us
4 2021-01-11T21:32:59.020Z 51.2774 100.4370 10.00 6.7 mww NaN 22.0 2.045 0.75 ... 2021-01-24T04:35:19.831Z 30 km SSW of Turt, Mongolia earthquake 7.0 1.7 0.039 62.0 reviewed us us

5 rows × 22 columns

The time is imported as text (strings)¶

In [8]:
type(eartt1.loc[0,'time_hour'])
Out[8]:
str

Changing time to datetime¶

In [9]:
eartt1['time_hour']=eartt1['time_hour'].str[:-5]
eartt1['time_hour']=pd.to_datetime(eartt1['time_hour'])

Time as the data frame's index¶

Create a new column called "type" to help us in the pivoting¶

In [10]:
eartt1=eartt1.set_index('time_hour')
eartt1['type']= 'ss'
eartt1.head(1)
Out[10]:
latitude longitude depth mag magType nst gap dmin rms net ... updated place type horizontalError depthError magError magNst status locationSource magSource
time_hour
2021-01-23 23:36:50 -61.825 -55.4937 9.58 6.9 mww NaN 19.0 1.55 0.61 us ... 2021-01-25T08:55:31.875Z South Shetland Islands ss 7.4 2.7 0.043 52.0 reviewed us us

1 rows × 21 columns

List number of earquakes by place, time, location, magnitude¶

In [11]:
start = eartt1.pivot_table('id',
                          index = ['place', 'time_hour',
                                   'latitude',
                                   'longitude', 
                                   'mag'
                                   ],
                          columns = 'type',
                          aggfunc='count').reset_index()




start.head()
Out[11]:
type place time_hour latitude longitude mag ss
0 0km SW of Loloan, Indonesia 2018-08-05 11:46:38 -8.2581 116.4375 6.9 1
1 0km W of Komerda, Indonesia 2016-02-12 10:02:24 -9.6338 119.4013 6.3 1
2 100 km ENE of Hachinohe, Japan 2020-12-20 17:23:22 40.8667 142.5814 6.3 1
3 100 km NW of Constitución, Chile 2020-11-22 00:54:56 -34.5978 -73.0596 6.1 1
4 100 km WNW of Kandrian, Papua New Guinea 2020-10-01 10:34:45 -5.9961 148.6656 6.1 1

Extract the hour from time_hour¶

In [12]:
start['time_hour']=pd.to_datetime(start['time_hour'])
start['time_hh']=start['time_hour'].dt.hour
start.head()
Out[12]:
type place time_hour latitude longitude mag ss time_hh
0 0km SW of Loloan, Indonesia 2018-08-05 11:46:38 -8.2581 116.4375 6.9 1 11
1 0km W of Komerda, Indonesia 2016-02-12 10:02:24 -9.6338 119.4013 6.3 1 10
2 100 km ENE of Hachinohe, Japan 2020-12-20 17:23:22 40.8667 142.5814 6.3 1 17
3 100 km NW of Constitución, Chile 2020-11-22 00:54:56 -34.5978 -73.0596 6.1 1 0
4 100 km WNW of Kandrian, Papua New Guinea 2020-10-01 10:34:45 -5.9961 148.6656 6.1 1 10

Choosing color depending on the magnitude for each Earthquake¶

3352FF Blue

FCFF33 Yellow

FF33EC Pink

FF334C Red

In [13]:
start.loc[start['mag']<7, 'fillcolor']='#3352FF ' # Earthquakes with magnitude less than 7  = blue color
start.loc[start['mag']>=7, 'fillcolor']='#FF33EC' # Earthquakes with magnitude bigger or equal than 7  = pink color
start.loc[start['mag']>=8, 'fillcolor']='#FCFF33' # Earthquakes with magnitude bigger or equal than 8  = yellow color
start.loc[start['mag']>=9, 'fillcolor']='#FF334C' # Earthquakes with magnitude bigger or equal than 9  = red color
In [14]:
start.shape
Out[14]:
(7488, 8)
In [15]:
start.head()
Out[15]:
type place time_hour latitude longitude mag ss time_hh fillcolor
0 0km SW of Loloan, Indonesia 2018-08-05 11:46:38 -8.2581 116.4375 6.9 1 11 #3352FF
1 0km W of Komerda, Indonesia 2016-02-12 10:02:24 -9.6338 119.4013 6.3 1 10 #3352FF
2 100 km ENE of Hachinohe, Japan 2020-12-20 17:23:22 40.8667 142.5814 6.3 1 17 #3352FF
3 100 km NW of Constitución, Chile 2020-11-22 00:54:56 -34.5978 -73.0596 6.1 1 0 #3352FF
4 100 km WNW of Kandrian, Papua New Guinea 2020-10-01 10:34:45 -5.9961 148.6656 6.1 1 10 #3352FF

Normalizing magnitude with max and min values¶

In [16]:
start['mag']= (start['mag'] - start['mag'].min())/(start['mag'].max()-start['mag'].min())
#df["A"] = (df["A"]-df["A"].min()) / (df["A"].max()-df["A"].min())
start['mag'].head()
Out[16]:
0    0.290323
1    0.096774
2    0.096774
3    0.032258
4    0.032258
Name: mag, dtype: float64

Function to create the features of the animation: time, location, icon to represent magnitude of Earthquake, etc¶

In [17]:
def create_geojson_features(df):
    features = []
    
    for _, row in df.iterrows():
        feature = {
            'type': 'Feature',
            'geometry': {
                'type':'Point', 
                'coordinates':[row['longitude'],row['latitude']]
            },
            'properties': {
                'time': pd.to_datetime(row['time_hh'], unit='h').__str__(),
                'style': {'color' : ''},
                'icon': 'circle',
                'iconstyle':{
                    'fillColor': row['fillcolor'],
                    'fillOpacity': 0.8,
                    'stroke': 'true',
                    'radius': row['mag']*10
                }
            }
        }
        features.append(feature)
    return features

Calling the Function create_geojson_features and using it in our data frame and get the geojson¶

In [18]:
start_geojson = create_geojson_features(start)
start_geojson[0]
Out[18]:
{'type': 'Feature',
 'geometry': {'type': 'Point', 'coordinates': [116.4375, -8.2581]},
 'properties': {'time': '1970-01-01 11:00:00',
  'style': {'color': ''},
  'icon': 'circle',
  'iconstyle': {'fillColor': '#3352FF ',
   'fillOpacity': 0.8,
   'stroke': 'true',
   'radius': 2.9032258064516143}}}

Creating Interactive And Animated Map¶

In [20]:
EQ_map = folium.Map(location = [2, -2],
                    tiles = "CartoDB Positron",
                    zoom_start = 2)
plugins.ScrollZoomToggler().add_to(EQ_map)

icon_plane = plugins.BeautifyIcon(
    icon="plane", border_color="#b3334f", text_color="#b3334f", icon_shape="triangle"
)

icon_number = plugins.BeautifyIcon(
    border_color="#00ABDC",
    text_color="#00ABDC",
    number=10,
    inner_icon_style="margin-top:0;",
)



folium.Marker(location=[50, -122], popup="Portland, OR", icon=icon_number).add_to(EQ_map)

plugins.Fullscreen(
    position="topright",
    title="Expand me",
    title_cancel="Exit me",
    force_separate_button=True,
).add_to(EQ_map)


TimestampedGeoJson(start_geojson,
                  period = 'PT1H',
                  duration = 'PT1H',
                  transition_time = 1000,
                  auto_play = True).add_to(EQ_map)

EQ_map
Out[20]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [ ]:
EQ_map.save('EQ_map.html', title='Earthquakes with Magnitude 6 and higher since 1968')
In [ ]: